8 changed files with 420 additions and 182 deletions
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46examples/shields/rl/11_minigridrl.py
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33examples/shields/rl/12_minigridrl_tune.py
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23examples/shields/rl/13_minigridsb.py
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34examples/shields/rl/14_train_eval.py
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65examples/shields/rl/15_train_eval_tune.py
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209examples/shields/rl/rllibutils.py
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68examples/shields/rl/sb3utils.py
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122examples/shields/rl/utils.py
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import gymnasium as gym |
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import numpy as np |
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import random |
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from minigrid.core.actions import Actions |
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from minigrid.core.constants import COLORS, OBJECT_TO_IDX, STATE_TO_IDX |
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from gymnasium.spaces import Dict, Box |
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from collections import deque |
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from ray.rllib.utils.numpy import one_hot |
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from helpers import get_action_index_mapping |
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from shieldhandlers import ShieldHandler |
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class OneHotShieldingWrapper(gym.core.ObservationWrapper): |
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def __init__(self, env, vector_index, framestack): |
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super().__init__(env) |
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self.framestack = framestack |
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# 49=7x7 field of vision; 16=object types; 6=colors; 3=state types. |
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# +4: Direction. |
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self.single_frame_dim = 49 * (len(OBJECT_TO_IDX) + len(COLORS) + len(STATE_TO_IDX)) + 4 |
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self.init_x = None |
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self.init_y = None |
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self.x_positions = [] |
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self.y_positions = [] |
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self.x_y_delta_buffer = deque(maxlen=100) |
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self.vector_index = vector_index |
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self.frame_buffer = deque(maxlen=self.framestack) |
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for _ in range(self.framestack): |
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self.frame_buffer.append(np.zeros((self.single_frame_dim,))) |
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self.observation_space = Dict( |
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{ |
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"data": gym.spaces.Box(0.0, 1.0, shape=(self.single_frame_dim * self.framestack,), dtype=np.float32), |
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"action_mask": gym.spaces.Box(0, 10, shape=(env.action_space.n,), dtype=int), |
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} |
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) |
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|
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def observation(self, obs): |
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# Debug output: max-x/y positions to watch exploration progress. |
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# print(F"Initial observation in Wrapper {obs}") |
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if self.step_count == 0: |
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for _ in range(self.framestack): |
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self.frame_buffer.append(np.zeros((self.single_frame_dim,))) |
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if self.vector_index == 0: |
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if self.x_positions: |
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max_diff = max( |
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np.sqrt( |
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(np.array(self.x_positions) - self.init_x) ** 2 |
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+ (np.array(self.y_positions) - self.init_y) ** 2 |
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) |
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) |
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self.x_y_delta_buffer.append(max_diff) |
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print( |
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"100-average dist travelled={}".format( |
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np.mean(self.x_y_delta_buffer) |
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) |
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) |
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self.x_positions = [] |
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self.y_positions = [] |
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self.init_x = self.agent_pos[0] |
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self.init_y = self.agent_pos[1] |
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self.x_positions.append(self.agent_pos[0]) |
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self.y_positions.append(self.agent_pos[1]) |
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image = obs["data"] |
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# One-hot the last dim into 16, 6, 3 one-hot vectors, then flatten. |
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objects = one_hot(image[:, :, 0], depth=len(OBJECT_TO_IDX)) |
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colors = one_hot(image[:, :, 1], depth=len(COLORS)) |
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states = one_hot(image[:, :, 2], depth=len(STATE_TO_IDX)) |
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all_ = np.concatenate([objects, colors, states], -1) |
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all_flat = np.reshape(all_, (-1,)) |
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direction = one_hot(np.array(self.agent_dir), depth=4).astype(np.float32) |
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single_frame = np.concatenate([all_flat, direction]) |
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self.frame_buffer.append(single_frame) |
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tmp = {"data": np.concatenate(self.frame_buffer), "action_mask": obs["action_mask"] } |
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return tmp |
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class MiniGridShieldingWrapper(gym.core.Wrapper): |
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def __init__(self, |
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env, |
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shield_creator : ShieldHandler, |
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shield_query_creator, |
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create_shield_at_reset=True, |
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mask_actions=True): |
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super(MiniGridShieldingWrapper, self).__init__(env) |
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self.max_available_actions = env.action_space.n |
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self.observation_space = Dict( |
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{ |
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"data": env.observation_space.spaces["image"], |
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"action_mask" : Box(0, 10, shape=(self.max_available_actions,), dtype=np.int8), |
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} |
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) |
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self.shield_creator = shield_creator |
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self.create_shield_at_reset = create_shield_at_reset |
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self.shield = shield_creator.create_shield(env=self.env) |
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self.mask_actions = mask_actions |
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self.shield_query_creator = shield_query_creator |
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print(F"Shielding is {self.mask_actions}") |
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def create_action_mask(self): |
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if not self.mask_actions: |
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ret = np.array([1.0] * self.max_available_actions, dtype=np.int8) |
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return ret |
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cur_pos_str = self.shield_query_creator(self.env) |
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# Create the mask |
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# If shield restricts action mask only valid with 1.0 |
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# else set all actions as valid |
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allowed_actions = [] |
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mask = np.array([0.0] * self.max_available_actions, dtype=np.int8) |
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if cur_pos_str in self.shield and self.shield[cur_pos_str]: |
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allowed_actions = self.shield[cur_pos_str] |
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zeroes = np.array([0.0] * len(allowed_actions), dtype=np.int8) |
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has_allowed_actions = False |
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for allowed_action in allowed_actions: |
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index = get_action_index_mapping(allowed_action.labels) # Allowed_action is a set |
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if index is None: |
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assert(False) |
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allowed = 1.0 |
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has_allowed_actions = True |
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mask[index] = allowed |
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else: |
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for index, x in enumerate(mask): |
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mask[index] = 1.0 |
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front_tile = self.env.grid.get(self.env.front_pos[0], self.env.front_pos[1]) |
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if front_tile is not None and front_tile.type == "key": |
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mask[Actions.pickup] = 1.0 |
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if front_tile and front_tile.type == "door": |
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mask[Actions.toggle] = 1.0 |
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# print(F"Mask is {mask} State: {cur_pos_str}") |
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return mask |
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def reset(self, *, seed=None, options=None): |
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obs, infos = self.env.reset(seed=seed, options=options) |
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if self.create_shield_at_reset and self.mask_actions: |
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self.shield = self.shield_creator.create_shield(env=self.env) |
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mask = self.create_action_mask() |
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return { |
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"data": obs["image"], |
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"action_mask": mask |
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}, infos |
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def step(self, action): |
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orig_obs, rew, done, truncated, info = self.env.step(action) |
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mask = self.create_action_mask() |
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obs = { |
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"data": orig_obs["image"], |
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"action_mask": mask, |
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} |
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return obs, rew, done, truncated, info |
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def shielding_env_creater(config): |
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name = config.get("name", "MiniGrid-LavaCrossingS9N3-v0") |
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framestack = config.get("framestack", 4) |
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args = config.get("args", None) |
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args.grid_path = F"{args.expname}_{args.grid_path}_{config.worker_index}.txt" |
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args.prism_path = F"{args.expname}_{args.prism_path}_{config.worker_index}.prism" |
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shielding = config.get("shielding", False) |
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shield_creator = MiniGridShieldHandler(grid_file=args.grid_path, |
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grid_to_prism_path=args.grid_to_prism_binary_path, |
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prism_path=args.prism_path, |
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formula=args.formula, |
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shield_value=args.shield_value, |
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prism_config=args.prism_config, |
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shield_comparision=args.shield_comparision) |
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probability_intended = args.probability_intended |
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probability_displacement = args.probability_displacement |
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env = gym.make(name, randomize_start=True,probability_intended=probability_intended, probability_displacement=probability_displacement) |
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env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query ,mask_actions=shielding != ShieldingConfig.Disabled) |
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env = OneHotShieldingWrapper(env, |
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config.vector_index if hasattr(config, "vector_index") else 0, |
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framestack=framestack |
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) |
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return env |
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def register_minigrid_shielding_env(args): |
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env_name = "mini-grid-shielding" |
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register_env(env_name, shielding_env_creater) |
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ModelCatalog.register_custom_model( |
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"shielding_model", |
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TorchActionMaskModel |
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) |
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@ -0,0 +1,68 @@ |
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import gymnasium as gym |
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import numpy as np |
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import random |
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|
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class MiniGridSbShieldingWrapper(gym.core.Wrapper): |
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def __init__(self, |
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env, |
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shield_creator : ShieldHandler, |
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shield_query_creator, |
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create_shield_at_reset = True, |
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mask_actions=True, |
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): |
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super(MiniGridSbShieldingWrapper, self).__init__(env) |
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self.max_available_actions = env.action_space.n |
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self.observation_space = env.observation_space.spaces["image"] |
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self.shield_creator = shield_creator |
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self.mask_actions = mask_actions |
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self.shield_query_creator = shield_query_creator |
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def create_action_mask(self): |
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if not self.mask_actions: |
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return np.array([1.0] * self.max_available_actions, dtype=np.int8) |
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cur_pos_str = self.shield_query_creator(self.env) |
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allowed_actions = [] |
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# Create the mask |
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# If shield restricts actions, mask only valid actions with 1.0 |
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# else set all actions valid |
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mask = np.array([0.0] * self.max_available_actions, dtype=np.int8) |
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if cur_pos_str in self.shield and self.shield[cur_pos_str]: |
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allowed_actions = self.shield[cur_pos_str] |
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for allowed_action in allowed_actions: |
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index = get_action_index_mapping(allowed_action.labels) |
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if index is None: |
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assert(False) |
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mask[index] = random.choices([0.0, 1.0], weights=(1 - allowed_action.prob, allowed_action.prob))[0] |
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else: |
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for index, x in enumerate(mask): |
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mask[index] = 1.0 |
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front_tile = self.env.grid.get(self.env.front_pos[0], self.env.front_pos[1]) |
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if front_tile and front_tile.type == "door": |
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mask[Actions.toggle] = 1.0 |
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return mask |
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def reset(self, *, seed=None, options=None): |
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obs, infos = self.env.reset(seed=seed, options=options) |
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shield = self.shield_creator.create_shield(env=self.env) |
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self.shield = shield |
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return obs["image"], infos |
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def step(self, action): |
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orig_obs, rew, done, truncated, info = self.env.step(action) |
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obs = orig_obs["image"] |
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return obs, rew, done, truncated, info |
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